The provision of floral resources for the enhancement of beneficial insect populations has shown promise as a strategy to enhance biological control and pollination in agroecosystems. One approach involves the provision of a single flower species while a second involves the multiple flower species, but the two have never been compared experimentally. Here we examine the influence of single and multiple species flower treatments on the abundance and foraging behaviour of key beneficial insects in two agricultural agroecosystems (broccoli and lucerne crops). The five flower treatments comprised buckwheat only, phacelia only, a simple mixture of buckwheat and phacelia, a complex mixture of buckwheat, phacelia and a commercial seed blend or the existing crop as a control. The abundance of bumble-bees (Bombus hortorum) and honey bees (Apis mellifera) was highest in the three treatments that contained phacelia, while hoverfly (Melanostoma fasciatum) numbers were high in all four flower treatments. Bumble-bees and honey bees probed almost exclusively phacelia flowers, even when provided with a choice of other flower species in the simple and complex mixture treatments. In contrast, hoverflies probed the flowers of all plant species in single and multiple species treatments, with no apparent difference in acceptance. However, in mixture treatments, the majority of individual bumble-bees, honey bees and hoverflies probed the flowers from only one species, despite the presence of alternative flower species. Our results illustrate how an appreciation of insect floral attractiveness can be used to customise the species composition of floral patches to potentially maximise biological control and pollination in targeted agroecosystems.
Physalia is a genus of pelagic colonial hydrozoans often known by common names such as 'Portuguese-man-of-war' or 'bluebottle'. Siphonophore systematists generally recognise only a single species in this genus, Physalia physalis, however the name Physalia utriculus is also still in common use, which has led to considerable taxonomic confusion. With some morphological variation between global regions there is the possibility that this genus holds a substantial amount of cryptic variation. We seek to examine the genetic structure of Physalia present in New Zealand coastal waters. Fifty-four specimens collected from 13 locations around New Zealand and Australia were sequenced for both mitochondrial cytochrome c oxidase I (COI) and the first internal transcribed spacer (ITS1) of the nuclear ribosomal cistron. Sequences were analysed using maximum likelihood and split decomposition neighbour networks to determine conflict between clans (the unrooted analog of clades). Three clans were identified from both the COI and ITS sequences. The results are complex and clans are not consistent between the two genes. Nevertheless, it seems that there is substantial cryptic diversity amongst Physalia present in New Zealand coastal waters.
Abstract-The apparent increase in number and magnitude of jellyfish blooms in the worlds oceans has lead to concerns over potential disruption and harm to global fishery stocks. Because of the potential harm that jellyfish populations can cause and to avoid impact it would be helpful to model jellyfish populations so that species presence or absence can be predicted. Data on the presence or absence of jellyfish of the genus Physalia was modelled using Multi-Layer Perceptrons (MLP) based on oceanographic data. Results indicated that MLP are capable of predicting the presence or absence of Physalia in two regions in New Zealand and of identifying significant biological variables.
Abstract. Environmental changes in oceanic conditions have the potential to cause jellyfish populations to rapidly expand leading to ecosystem level repercussions. To predict potential changes it is necessary to understand how such populations are influenced by oceanographic conditions. Data recording the presence or absence of jellyfish of the genus Physalia at beaches in the West Auckland region of New Zealand were modelled using Multi-Layer Perceptrons (MLP) with time lagged oceanographic data as input data. Results showed that MLP models were able to generalise well based on Kappa statistics and gave good predictions of the presence or absence of Physalia. Moreover, an analysis of the network contributions indicated an interaction between wave and wind variables at different time intervals can promote or inhibit the occurrence of Physalia.
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